Performance Improvement of a Heat Sink for Battery Thermal Management System
Bibliographic record
Abstract
Abstract Dissipating heat generated by onboard battery systems is a key thermal management challenge for hybrid/electric aircraft. The National Research Council Canada is collaborating with Calogy Solutions, Ltd. (Sherbrooke, Quebec, Canada) to support their development of a novel aviation battery module. The goal of the project is to optimize the heat sink design for battery thermal management system by minimizing thermal resistance as well as reducing overall weight, considering fabrication cost to be used by the industry. In this research, the original heat sink used by Calogy (plate-fin type) was numerically simulated using commercial software and the results were compared with the experiment. This was done to validate the numerical simulation was accurate enough to be used for future simulations. The heat transfer of the heat sink was enhanced by optimizing fin dimensions and spacing, and fin design modification (a first in this application), based on system analysis of a kW scale hybrid/electric aircraft. The selected model as the optimized heat sink resulted in about 34% improvement in heat transfer coefficient, and about 24% reduction in weight comparing with the original plate-fin type heat sink.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".